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  • Conference paper
    Patrick KCA, Imtiaz SA, Bowyer S, Rodriguez Villegas Eet al., 2016,

    An Algorithm for Automatic Detection of Drowsiness for Use inWearable EEG Systems

    , IEEE EMBC 2016, Publisher: IEEE, ISSN: 1557-170X

    Lack of proper restorative sleep can induce sleepinessat odd hours making a person drowsy. This onset of drowsinesscan be detrimental for the individual in a number of waysif it happens at an unwanted time. For example, drowsinesswhile driving a vehicle or operating heavy machinery poses athreat to the safety and wellbeing of individuals as well as thosearound them. Timely detection of drowsiness can prevent theoccurrence of unfortunate accidents thereby improving roadand work environment safety. In this paper, by analyzing theelectroencephalographic (EEG) signals of human subjects inthe frequency domain, several features across different EEGchannels are explored. Of these, three features are identified tohave a strong correlation with drowsiness. A weighted sum ofthese defining features, extracted from a single EEG channel,is then used with a simple classifier to automatically separatethe state of wakefulness from drowsiness. The proposed algorithmresulted in drowsiness detection sensitivity of 85% andspecificity of 93%.
  • Journal article
    Imtiaz SA, Mardell JAMES, Saremi-Yarahmadi SIAVASH, Rodriguez Villegas ESTHERet al., 2016,

    ECG Artefact Identification and Removal in mHealth Systems for Continuous Patient Monitoring

    , Healthcare Technology Letters, Vol: 3, Pages: 171-176, ISSN: 2053-3713

    Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automaticallyprocessed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patientmovement, sensor placement and interference from other sources. Because of the large volume of data these artefacts need to be automaticallyidentified so that the analysis systems and doctors are aware of them while making medical diagnosis. This paper explores three importantfactors that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality,interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm while the third isparticularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filteringalgorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstratehow different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.
  • Journal article
    Pramono R, Imtiaz SA, Rodriguez Villegas ESTHER, 2016,

    A Cough-Based Algorithm for Automatic Diagnosis of Pertussis

    , PLOS One, Vol: 11, ISSN: 1932-6203

    Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control.
  • Conference paper
    Imtiaz SA, Rodriguez-Villegas E, 2015,

    Automatic sleep staging using state machine-controlled decision trees.

    , 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 378-381, ISSN: 1557-170X

    Automatic sleep staging from a reduced number of channels is desirable to save time, reduce costs and make sleep monitoring more accessible by providing home-based polysomnography. This paper introduces a novel algorithm for automatic scoring of sleep stages using a combination of small decision trees driven by a state machine. The algorithm uses two channels of EEG for feature extraction and has a state machine that selects a suitable decision tree for classification based on the prevailing sleep stage. Its performance has been evaluated using the complete dataset of 61 recordings from PhysioNet Sleep EDF Expanded database achieving an overall accuracy of 82% and 79% on training and test sets respectively. The algorithm has been developed with a very small number of decision tree nodes that are active at any given time making it suitable for use in resource-constrained wearable systems.
  • Conference paper
    Imtiaz SA, Rodriguez-Villegas E, 2015,

    An open-source toolbox for standardized use of PhysioNet Sleep EDF Expanded Database.

    , 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC, Publisher: IEEE, Pages: 6014-6017, ISSN: 1557-170X

    PhysioNet Sleep EDF database has been the most popular source of data used for developing and testing many automatic sleep staging algorithms. However, the recordings from this database has been used in an inconsistent fashion. For example, arbitrary selection of start and end times from long term recordings, data-hypnogram mismatches, different performance metrics and hypnogram conversion from R&K to AASM. All these differences result in different data sections and performance metrics being used by researchers thereby making any direct comparison between algorithms very difficult. Recently, a superset of this database has been made available on PhysioNet, known as the Sleep EDF Expanded Database which includes 61 recordings. This provides an opportunity to standardize the way in which signals from this database should be used. With this goal in mind, we present in this paper a toolbox for automatically downloading and extracting recordings from the Sleep EDF Expanded database and converting them to a suitable format for use in MATLAB. This toolbox contains functions for selecting appropriate data for sleep analysis (based on our previous recommendations for sleep staging), hypnogram conversion and computation of performance metrics. Its use makes it simpler to start using the new sleep database and also provides a foundation for much-needed standardization in this research field.
  • Journal article
    Imtiaz SA, Logesparan L, Rodriguez-Villegas E, 2015,

    Performance-Power Consumption Tradeoff in Wearable Epilepsy Monitoring Systems

    , IEEE Journal of Biomedical and Health Informatics, Vol: 19, Pages: 1019-1028, ISSN: 2168-2208

    Automated seizure detection methods can be used to reduce time and costs associated with analyzing large volumes of ambulatory EEG recordings. These methods however have to rely on very complex, power hungry algorithms, implemented on the system backend, in order to achieve acceptable levels of accuracy. In size, and therefore power-constrained EEG systems, an alternative approach to the problem of data reduction is online data selection, in which simpler algorithms select potential epileptiform activity for discontinuous recording but accurate analysis is still left to a medical practitioner. Such a diagnostic decision support system would still provide doctors with information relevant for diagnosis while reducing the time taken to analyze the EEG. For wearable systems with limited power budgets, data selection algorithm must be of sufficiently low complexity in order to reduce the amount of data transmitted and the overall power consumption. In this paper, we present a low-power hardware implementation of an online epileptic seizure data selection algorithm with encryption and data transmission and demonstrate the tradeoffs between its accuracy and the overall system power consumption. We demonstrate that overall power savings by data selection can be achieved by transmitting less than 40% of the data. We also show a 29% power reduction when selecting and transmitting 94% of all seizure events and only 10% of background EEG.
  • Journal article
    Chen G, Imtiaz SA, Aguilar-Pelaez E, Rodriguez-Villegas Eet al., 2015,

    Algorithm for heart rate extraction in a novel wearable acoustic sensor.

    , Healthcare Technology Letters, Vol: 2, Pages: 28-33, ISSN: 2053-3713

    Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds - S1 and S2 - that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S1 and S2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long-term wearable vital signs monitoring.
  • Journal article
    Imtiaz SA, Rodriguez-Villegas E, 2014,

    A low computational cost algorithm for REM sleep detection using single channel EEG.

    , Ann Biomed Eng, Vol: 42, Pages: 2344-2359

    The push towards low-power and wearable sleep systems requires using minimum number of recording channels to enhance battery life, keep processing load small and be more comfortable for the user. Since most sleep stages can be identified using EEG traces, enormous power savings could be achieved by using a single channel of EEG. However, detection of REM sleep from one channel EEG is challenging due to its electroencephalographic similarities with N1 and Wake stages. In this paper we investigate a novel feature in sleep EEG that demonstrates high discriminatory ability for detecting REM phases. We then use this feature, that is based on spectral edge frequency (SEF) in the 8-16 Hz frequency band, together with the absolute power and the relative power of the signal, to develop a simple REM detection algorithm. We evaluate the performance of this proposed algorithm with overnight single channel EEG recordings of 5 training and 15 independent test subjects. Our algorithm achieved sensitivity of 83%, specificity of 89% and selectivity of 61% on a test database consisting of 2221 REM epochs. It also achieved sensitivity and selectivity of 81 and 75% on PhysioNet Sleep-EDF database consisting of 8 subjects. These results demonstrate that SEF can be a useful feature for automatic detection of REM stages of sleep from a single channel EEG.
  • Journal article
    Rodriguez-Villegas E, Chen G, Radcliffe J, Duncan Jet al., 2014,

    A pilot study of a wearable apnoea detection device

    , BMJ Open, Vol: 4, Pages: 1-9, ISSN: 2044-6055

    Rationale: Current techniques for monitoring patients for apnoea suffer from significant limitations. These include insufficient availability to meet diagnostic needs, cost, accuracy of results in the presence of artefacts and difficulty of use in unsupervised conditions.Objectives: We created and clinically tested a novel miniature medical device that targets overcoming these limitations.Methods: We studied 20 healthy control participants and 10 patients who had been referred for sleep apnoea diagnosis. The performances of the new system and also of the Food and Drug Administration (FDA) approved SOMNO clinical system, conventionally used for sleep apnoea diagnosis were evaluated under the same conditions. Both systems were tested during a normal night of sleep in controls and patients. Their performances were quantified in terms of detection of apnoea and hypopnoea in individual 10 s epochs, which were compared with scoring of signals by a blinded clinician.Main results: For spontaneous apnoeas during natural sleep and considering the clinician scorer as the gold standard, the new wearable apnoea detection device had 88.6% sensitivity and 99.6% specificity. In comparison the SOMNO system had 14.3% sensitivity and 99.3% specificity. The novel device had been specifically designed to detect apnoea, but if apnoea and hypopnoea during sleep were both considered in the assessment, the sensitivity and specificity were 77.1% and 99.7%, respectively, versus 54% and 98.5%, respectively, for the SOMNO.Conclusions: The performance of the novel device compares very well to the scoring by an experienced clinician even in the presence of breathing artefacts, in this small pilot study. This can potentially make it a real solution for apnoea home monitoring.
  • Conference paper
    Imtiaz SA, Rodriguez-Villegas E, 2014,

    Recommendations for Performance Assessment of Automatic Sleep Staging Algorithms

    , 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5044-5047, ISSN: 1557-170X

    A number of automatic sleep scoring algorithms have been published in the last few years. These can potentially help save time and reduce costs in sleep monitoring. However, the use of both R&K and AASM classification, different databases and varying performance metrics makes it extremely difficult to compare these algorithms. In this paper, we describe some readily available polysomnography databases and propose a set of recommendations and performance metrics to promote uniform testing and direct comparison of different algorithms. We use two different polysomnography databases with a simple sleep staging algorithm to demonstrate the usage of all recommendations and presentation of performance results. We also illustrate how seemingly similar results using two different databases can have contrasting accuracies in different sleep stages. Finally, we show how selection of different training and test subjects from the same database can alter the final performance results.
  • Journal article
    Rodriguez-Villegas E, Logesparan L, Casson AJ, 2014,

    A Low Power Linear Phase Programmable Long Delay Circuit

    , IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 8, Pages: 432-441, ISSN: 1932-4545
  • Journal article
    Imtiaz SA, Casson AJ, Rodriguez-Villegas E, 2014,

    Compression in Wearable Sensor Nodes: Impacts of Node Topology

    , IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 61, Pages: 1080-1090, ISSN: 0018-9294
  • Journal article
    Casson AJ, Rodriguez-Villegas E, 2014,

    Nanowatt multi-scale continuous wavelet transform chip

    , ELECTRONICS LETTERS, Vol: 50, Pages: 153-154, ISSN: 0013-5194
  • Journal article
    Saraswat R, Rodriguez-Villegas E, 2014,

    Low Power EMI Mitigation Strategies Utilizing Non-PLL Based Periodic and Chaotic SSC Profiles for Implanted Devices

    , 2014 8TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION AND COMMUNICATION TECHNOLOGY (ISMICT), ISSN: 2326-828X
  • Conference paper
    Imtiaz SA, Rodriguez-Villegas E, 2014,

    Evaluating the use of line length for automatic sleep spindle detection

    , 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5024-5027, ISSN: 1557-170X
  • Conference paper
    Hizon JRE, Rodriguez-Villegas E, 2014,

    A Reconfigurable FGMOS based OTA-C Filter

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2093-2096, ISSN: 0271-4302
  • Conference paper
    Saraswat R, Rodriguez-Villegas E, 2014,

    Chaotic Inductively Coupled Non-PLL Low Emission Transmitter for Implanted Devices

    , IEEE 5th Latin American Symposium on Circuits and Systems (LASCAS), Publisher: IEEE, ISSN: 2330-9954
  • Conference paper
    Imtiaz SA, Saremi-Yarahmadi S, Rodriguez-Villegas E, 2013,

    Automatic detection of sleep spindles using Teager energy and spectral edge frequency

    , IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 262-265

    Sleep spindles are the hallmark of N2 stage of sleep. They are transient waveforms observed on sleep electroencephalogram and their identification is required for sleep staging. Due to the large number of sleep spindles appearing on an overnight sleep EEG, automating the detection of sleep spindles would be desirable, not only to save specialist time but also for fully automated sleep staging systems. A simple algorithm for automatic sleep spindle detection is presented in this paper using only one channel of EEG input. This algorithm uses Teager energy and spectral edge frequency to mark sleep spindles and results in a sensitivity of 80% and specificity of about 98%. It is also shown that more than 91% of spindles detected by the algorithm were in N2 and N3 stages combined.
  • Conference paper
    Logesparan L, Casson AJ, Imtiaz SA, Rodriguez-Villegas Eet al., 2013,

    Discriminating between best performing features for seizure detection and data selection

    , 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 1692-1695, ISSN: 1557-170X

    Seizure detection algorithms have been developed to solve specific problems, such as seizure onset detection, occurrence detection, termination detection and data selection. It is thus inherent that each type of seizure detection algorithm would detect a different EEG characteristic (feature). However most feature comparison studies do not specify the seizure detection problem for which their respective features have been evaluated. This paper shows that the best features/algorithm bases are not the same for all types of algorithms but depend on the type of seizure detection algorithm wanted. To demonstrate this, 65 features previously evaluated for online seizure data selection are re-evaluated here for seizure occurrence detection, using performance metrics pertinent to each seizure detection type whilst keeping the testing methodology the same. The results show that the best performing features/algorithm bases for data selection and occurrence detection algorithms are different and that it is more challenging to achieve high detection accuracy for the former seizure detection type. This paper also provides a comprehensive evaluation of the performance of 65 features for seizure occurrence detection to aid future researchers in choosing the best performing feature(s) to improve seizure detection accuracy.
  • Journal article
    Pinuela M, Yates DC, Lucyszyn S, Mitcheson PDet al., 2013,

    Maximizing DC-to-Load Efficiency for Inductive Power Transfer

    , IEEE Transactions on Power Electronics, Vol: 28, Pages: 2437-2447, ISSN: 0885-8993

    Inductive Power Transfer (IPT) systems for transmitting tens to hundreds of watts have been reported for almost a decade. Most of the work has concentrated on the optimization of the link efficiency and have not taken into account the efficiency of the driver. Class-E amplifiers have been identified as ideal drivers for IPT applications, but their power handling capabilityat tens of MHz has been a crucial limiting factor, since the load and inductor characteristics are set by the requirements of the resonant inductive system. The frequency limitation of the driver restricts the unloaded Q factor of the coils and thus the link efficiency. With a suitable driver, copper coilunloaded Q factors of over 1,000 can be achieved in the low MHz region, enabling a cost-effective high Q coil assembly. The system presented in this paper alleviates the use of heavy andexpensive field-shaping techniques by presenting an efficient IPT system capable of transmitting energy with a dc-to-load efficiency above 77% at 6 MHz across a distance of 30 cm. To the authorsknowledge this is the highest dc-to-load efficiency achieved for an IPT system without introducing restrictive coupling factor enhancement techniques.
  • Conference paper
    Rajagopal MK, Rodriguez-Villegas E, 2013,

    Towards Wearable Sleep Diagnostic Systems for Point-of-Care Applications

    , 1st IEEE-EMBS Special Topic Conference on Point-of-Care (POCT) Healthcare Technologies (PHT), Publisher: IEEE, Pages: 26-29
  • Conference paper
    Saraswat R, Rodriguez-Villegas E, 2013,

    A Low Emission, Low Power Non-Linear Frequency Modulation Based Transmitter For Implanted Devices

    , 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 826-829, ISSN: 1557-170X
  • Journal article
    Kafal i Ö, Bromuri S, Sindlar M, van der Weide T, Aguilar Pelaez E, Schaechtle U, Alves B, Zufferey D, Rodriguez-Villegas E, Schumacher MI, otherset al., 2013,

    Commodity 12: A smart e-health environment for diabetes management

    , Journal of Ambient Intelligence and Smart Environments, Vol: 5, Pages: 479-502
  • Journal article
    Abdulghani AM, Casson AJ, Rodriguez-Villegas E, 2012,

    Compressive sensing scalp EEG signals: implementations and practical performance

    , MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, Vol: 50, Pages: 1137-1145, ISSN: 0140-0118
  • Conference paper
    Casson AJ, Rodriguez-Villegas E, 2012,

    Signal agnostic compressive sensing for body area networks: Comparison of signal reconstructions

    , 34th international conference of the IEEE Engineering in Medicine and Biology Society, Pages: 4497-4500

    Compressive sensing is a lossy compression technique that is potentially very suitable for use in power constrained sensor nodes and Body Area Networks as the compression process has a low computational complexity. This paper investigates the reconstruction performance of compressive sensing when applied to EEG, ECG, EOG and EMG signals; establishing the performance of a signal agnostic compressive sensing strategy that could be used in a Body Area Network monitoring all of these. The results demonstrate that the EEG, ECG and EOG can all be reconstructed satisfactorily, although large inter- and intra- subject variations are present. EMG signals are not well reconstructed. Compressive sensing may therefore also find use as a novel method for the identification of EMG artefacts in other electro-physiological signals.
  • Conference paper
    Logesparan L, Casson AJ, Rodriguez-Villegas E, 2012,

    Improving seizure detection performance reporting: Analysing the duration needed for a detection

    , 34th international conference of the IEEE Engineering in Medicine and Biology Society, Pages: 1069-1072

    Improving seizure detection performance relies not only on novel signal processing approaches but also on new accurate, reliable and comparable performance reporting to give researchers better and fairer tools for understanding the true algorithm operation. This paper investigates the sensitivity of current performance metrics to the duration of data that must be marked as candidate seizure activity before a seizure detection is made. The results demonstrate that not all metrics are insensitive to this high level choice in the algorithm design, and provide new approaches for comparing between reported algorithm performances in a robust and reliable manner.
  • Journal article
    Logesparan L, Casson AJ, Rodriguez-Villegas E, 2012,

    Optimal features for online seizure detection

    , Medical and Biological Engineering and Computing, Vol: 50, Pages: 659-669, ISSN: 0140-0118

    This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for on-line, real-time analysis. The discriminative performance of 65 previously reported features has been evaluated in terms of sensitivity, specificity, Area Under the sensitivity-specificity Curve (AUC), and relative computational complexity, on 47 seizures (split in 2698 2 s sections) in over 172 hours of scalp EEG from 24 adults. The best performing features are line length and relative power in the 12.5–25 Hz band. Relative power has a better seizure detection performance (AUC=0.83; line length AUC=0.77), but is calculated after the Discrete Wavelet Transform and is thus more computationally complex. Hence, relative power achieves the best performance for offline detection, whilst line length would be preferable for online low complexity detection. These results, from the largest systematic study of seizure detection features, aid future researchers in selecting an optimal set of features when designing algorithms for both standard offline detection and new online low computational complexity detectors.
  • Patent
    Rodriguez-Villegas E, 2012,

    Feature characterization for breathing monitor
  • Conference paper
    Hizon JR, 2012,

    A Compact Linearly Tunable Low Voltage Triode OTA Using Self-Cascodes

    , IEEE International Symposium on Circuits and Systems
  • Conference paper
    Mardell J, Witkowski M, Spence R, 2012,

    An Interface for Visual Inspection based on Image Segmentation

    , International Working Conference on Advanced Visual Interfaces (AVI), Publisher: ASSOC COMPUTING MACHINERY, Pages: 697-700
  • Journal article
    Shad A, Rodriguez-Villegas E, 2012,

    Proof of concept of a shoe based human activity monitor.

    , Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2012, Pages: 6398-6401

    This paper presents the proof of concept of a low power, low cost, wearable activity monitor. The functionality of the system is based on accurate stride detection from signals generated by two force sensing resistors integrated within a normal shoe. A novel algorithm is proposed that is able to differentiate between walking and non-walking activities with high accuracy. The performance of the proof of concept system was validated in five subjects who underwent five repetitions of three different speed walking tests, and five repetitions of five non-walking artefact generating tests. The system achieved a total sensitivity of 96% with 98% specificity and an overall accuracy of 94%.
  • Conference paper
    Hizon JRE, Rodriguez-Villegas E, 2012,

    A High Transconductance Efficiency FGMOS OTA for g<sub>m</sub>-C Ladder Filters

    , 55th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Publisher: IEEE, Pages: 105-108, ISSN: 1548-3746
  • Journal article
    Aguilar-Pelaez E, Chen G, Rodriguez-Villegas E, 2012,

    Technique for interference reduction in battery powered physiological monitoring devices
  • Journal article
    He C, Kiziroglou ME, Yates DC, 2011,

    A MEMS Self-Powered Sensor and RF Transmission Platform for WSN Nodes

    , IEEE Sensors Journal, Vol: 11, Pages: 3437-3445
  • Conference paper
    Saremi-Yarahmadi S, Fobelets K, Toumazou C, 2011,

    Coupled RF Inductive sensors for monitoring the pH of electrolyte solutions

    , International conference on dielectric liquids
  • Conference paper
    Logesparan L, Casson AJ, Rodriguez-Villegas E, 2011,

    Performance metrics for characterization of a seizure detection algorithm for offline and online use

    , 5th International Workshop on Seizure Prediction

    Purpose: To select appropriate previously reported performance metrics to evaluate a new seizure detection algorithm for offline and online analysis, and thus quantify any performance variation between these metrics.Methods: Traditional offline algorithms mark out any EEG section (epoch) of a seizure (event), so that neurologists only analyze the detected and adjacent sections. Thus, offline algorithms could be evaluated using number of correctly detected events, or event-based sensitivity (SEVENT), and epoch-based specificity (percentage of incorrectly detected background epochs). In contrast, online seizure detection (especially, data selection) algorithms select for transmission only the detected EEG sections and hence need to detect the entire duration of a seizure. Thus, online algorithms could be evaluated using percentage of correctly detected seizure duration, or epoch-based sensitivity (SEPOCH), and epoch-based specificity. Here, a new seizure detection algorithm is evaluated using the selected performance metrics for epoch duration ranging from 1s to 60s.Results: For 1s epochs, the area under the event-based sensitivity-specificity curve was 0.95 whilst SEPOCH achieves 0.81. This difference is not surprising, as intuitively, detecting any epoch within a seizure is easier than detecting every epoch - especially as seizures evolve over time. For longer epochs of 30s or 60s, SEVENT falls to 0.84 and 0.82 respectively and SEPOCH reduces to 0.76. Here, decreased SEVENT shows that fewer seizures are detected, possibly due to easy-to-detect short seizure sections being masked by surrounding EEG. However, detecting one long epoch constitutes a larger percentage of a seizure than a shorter one and thus SEPOCH does not decrease proportionately.Conclusions: Traditional offline and online seizure detection algorithms require different metrics to effectively evaluate their performance for their respective applications. Using such metrics, it has been shown that a decre
  • Conference paper
    Logesparan L, Casson AJ, Rodriguez-Villegas E, 2011,

    Assessing the impact of signal normalization: Preliminary results on epileptic seizure detection

    , Alex Casson, Publisher: IEEE, Pages: 1439-1442

    Signal normalization is an essential part of patient independent algorithms, for example to correct for variations in signal amplitude from different parts of the body, prior to applying a fixed threshold for event detection. Multiple methods for providing the required normalization are available. This paper presents a systematic investigation into the effects of five different methods using epileptic seizure detection from the EEG as an illustration case. It is found that, whilst normalization is essential, four of the considered methods actually decrease the ability to detect seizures, counteracting the algorithm aim. For optimal detection performance the effects of the signal normalization illustrated here should be incorporated into future algorithm designs.
  • Journal article
    Casson AJ, Rodriguez-Villegas E, 2011,

    Utilising noise to improve an interictal spike detector

    , J. Neurosci. Methods, Vol: 201, Pages: 262-268

    Dithering is the process of intentionally adding artificially generated noise to an otherwise uncorrupted signal to actually improve the performance of an end overall system. This article demonstrates that a dithering procedure can be used to improve the performance of an EEG interictal spike detection algorithm. Using a previously reported algorithm, by adding varying amounts of artificially generated noise to the input EEG signals the effect on the algorithm detection performance is investigated. A new stochastic resonance result is found whereby the spike detection performance improves by up to 4.3% when small amounts of corrupting noise, below 20 $\mu$VRMS, are added to the input data. This result is of use for improving the detection performance of algorithms, and the result also affects the dynamic range required for the hardware implementation of such algorithms in low power, portable EEG systems.
  • Book chapter
    Casson AJ, Rodriguez-Villegas E, 2011,

    Interfacing biology and circuits: quantification and performance metrics

    , Integrated Bio-Microsystems, Editors: Iniewski, Publisher: Wiley, Pages: 1-33, ISBN: 9780470641903
  • Journal article
    Rodriguez-Villegas E, Casson AJ, Corbishley P, 2011,

    A Subhertz Nanopower Low-Pass Filter

    , IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 58, Pages: 351-355, ISSN: 1549-7747
  • Conference paper
    Saremi-Yarahmadi S, Fobelets K, Toumazou C, 2011,

    Coupled RF inductive sensors for monitoring the conductivity of electrolyte solutions

    , International symposium on medical information & communication technology
  • Journal article
    Casson AJ, Rodriguez-Villegas E, 2011,

    A 60 pW gmC Continuous Wavelet Transform Circuit for Portable EEG Systems

    , IEEE Journal of Solid-State Circuits, Vol: 46, Pages: 1406-1415

    This paper presents a low power, low voltage and low frequency bandpass filter implementation of a continuous wavelet transform (CWT) for use with physiological signals in the electroencephalogram (EEG) range (1–150 $mu$V, 1–70 Hz bandwidth). Experimental results are presented for a 1 V, 7th order g$_{m}$ C filter based CWT with filter center frequencies ranging from 1 to 64 Hz.
  • Journal article
    He C, Kiziroglou ME, Yates DC, Yeatman EMet al., 2011,

    A MEMS self-powered sensor and RF transmission platform for WSN nodes

    , Sensors Journal, IEEE, Vol: 11, Pages: 3437-3445, ISSN: 1530-437X
  • Journal article
    Logesparan L, Rodriguez-Villegas E, 2011,

    A novel phase congruency based algorithm for online data reduction in ambulatory EEG systems

    , IEEE Transactions on Biomedical Engineering, Vol: 58, Pages: 2825-2834, ISSN: 0018-9294

    Abstract—Real signals are often corrupted by noise with a power spectrum variable over time. In applications involving these signals, it is expected that dynamically estimating and correcting for this noise would increase the amount of useful information extracted from the signal. One such application is scalp EEG monitoring in epilepsy, where electrical activity generated by cranio-facial muscles obscure the measured brainwaves. This paper presents a data selection algorithm based on phase congruency to identify interictal spikes from background EEG; together with a novel statistical method that allows a more comprehensive trade-off based quantitative comparison of two algorithms which have been tested at a fixed threshold in the same database. Here, traditional phase congruency has been modified to incorporate a dynamic estimate of muscle activity present in the input scalp EEG signal. The proposed algorithmachieves 50% data reduction whilst detecting more than 80% of interictal spikes. This represents a significant improvement overthe state-of-the-art denoising method for phase congruency.
  • Conference paper
    Brinded T, Mardell J, Witkowski M, Spence Ret al., 2011,

    The Effects of Image Speed and Overlap on Image Recognition

    , 15th International Conference on Information Visualisation (IV), Publisher: IEEE COMPUTER SOC, Pages: 3-11, ISSN: 1550-6037
  • Conference paper
    Casson AJ, Rodriguez-Villegas E, 2011,

    Algorithms and circuits for truly wearable physiological monitoring

    , 33rd international conference of the IEEE Engineering in Medicine and Biology Society
  • Journal article
    Casson AJ, Rodriguez-Villegas E, 2011,

    "A review and modern approach to LC ladder synthesis,"

    , Journal of Low Power Electronics and Applications, Vol: 1, Pages: 20-44
  • Conference paper
    Mardell J, Witkowski M, Spence R, 2011,

    Gaze-contingent enhancements for a visual search and rescue task.

    , Publisher: ACM, Pages: 109-109
  • Conference paper
    Logesparan L, Rodriguez Villegas E, 2010,

    Improving phase congruency for EEG data reduction

    , 32nd Annual International Conference of the IEEE EMBS, Publisher: IEEE, Pages: 642-645, ISSN: 1557-170X

    Real signals are often corrupted by noise. In applications where the noise power spectrum is variable with time, dynamic noise estimation and compensation can potentially improve the performance of signal processing algorithms. One such application is scalp EEG monitoring in epilepsy, where the electrical activity generated by cranio-facial muscle contraction and expansion, often obscures the measured brainwave signals. This work presents a data reduction algorithm which is based on differentiating interictal from normal background activity, in epileptic scalp EEG signals, using a modified phase congruency technique. The modification is based on dynamically estimating muscle activity from the signal and incorporating this estimation in phase congruency computations. The proposed algorithm identifies 90%of interictal spikes whilst transmitting only 45% of EEG data. This is in the order of 15% improvement in data reduction when compared to the performance obtained with the state–of–the–art denoised phase congruency—which calculates a constant noise threshold—applied to the same dataset.
  • Conference paper
    Abdulghani AM, Rodriguez Villegas E, 2010,

    Compressive sensing: From "compressing while sampling" to 'compressing and securing while sampling

    , 32nd Annual International Conference of the IEEE EMBS, Publisher: IEEE, Pages: 1127-1130, ISSN: 1557-170X

    In a traditional signal processing system sampling is carried out at a frequency which is at least twice the highest frequency component found in the signal. This is in order to guarantee that complete signal recovery is later on possible. The sampled signal can subsequently be subjected to further processing leading to, for example, encryption and compression. This processing can be computationally intensive and, in the case of battery operated systems, unpractically power hungry. Compressive sensing has recently emerged as a new signal sampling paradigm gaining huge attention from the research community. According to this theory it can potentially be possible to sample certain signals at a lower than Nyquist rate without jeopardizing signal recovery. In practical terms this may provide multi-pronged solutions to reduce some systems computational complexity. In this work, information theoretic analysis of real EEG signals is presented that shows the additional benefits of compressive sensing in preserving data privacy. Through this it can then be established generally that compressive sensing not only compresses but also secures while sampling.

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